Is there a correlation between cloud phase
and surface snowfall rate in GCMs?
Franziska Hellmuth1, Anne Fouilloux1, Anne Sophie Daloz2, Trude Storelvmo1
1 Department of Geosciences, University of Oslo, Norway
2 Center for International Climate Research (CICERO), Norway franziska.hellmuth@geo.uio.no
●Why is it important to present cloud phase and
snowfall accurately? Is snowfall bias in GCMs
relatable to cloud phase bias?
●How can we use GCM data using Pangeo?
●What was not provided by Pangeo?
Mixed phase clouds are
not well represented in
GCMs.
Ice formation influences
radiative effect,
precipitation formation,
and cloud lifetime.
Objective
Ice water path/Liquid water path (ERA5 - CMIP6)
(g m-2)
Ice water path overestimate Liquid water path underestimate
Ice Liquid
30 year, season: DJF
●Why is it important to present cloud phase and snowfall
accurately? Is snowfall bias in GCMs relatable to cloud phase
bias?
●How can we use GCM data using Pangeo?
●What was not provided by Pangeo?
CMIP6 models and variables (pangeo.io)
17 CMIP6 models
Horizontal resolution 100 km
●Why is it important to present cloud phase and snowfall
accurately? Is snowfall bias in GCMs relatable to cloud phase
bias?
●How can we use GCM data using Pangeo?
●What was not provided by Pangeo?
Horizontal grid (CMIP6 to NorESM2-MM)
https://tinyurl.com/regridder
Vertical grid (hybrid-σ-pressure to isobaric-pressure)
https://tinyurl.com/hybridtopressure
●Pangeo provides good amount of functions
○Vertical interpolation:
geocat.comp.interp_hybrid_to_pressure
○Horizontal interpolation: xesmf.Regridder
franziska.hellmuth@geo.uio.no, EGU22-8037
Key points
Next steps
●Find mixed-phase clouds in CMIP6
●Relate mixed-phase clouds to surface snowfall
●Include satellite data (CloudSat)
My Notebooks